DataRobot-Research/drdocs huggingface dataset:
The execution environment limit allows you to control how many custom model environments a user can add to the [Custom Model Workshop](custom-model-workshop/index). In addition, the execution environment _version_ limit allows you to control how many versions a user can add to _each_ of those environments. These limits can be:

1. **Directly applied to the user**: Set in a user's permissions. Overrides the limits set in the group and organization permissions.

2. **Inherited from a user group**: Set in the permissions of the group a user belongs to. Overrides the limits set in organization permissions.

3. **Inherited from an organization**: Set in the permissions of the organization a user belongs to.

If the environment or environment version limits are defined for an organization or a group, the users within that organization or group inherit the defined limits. However, a more specific definition of those limits at a lower level takes precedence. For example, an organization may have the environment limits set to 5, a group to 4, and the user to 3; in this scenario, the final limit for the individual user is 3.

For more information on adding custom model execution environments, see the [Custom model environments documentation](custom-environments).

To manage the execution environment limits in the platform settings:
!!! info "Availability information"
    The OpenAI integration is off by default. Contact your DataRobot representative or administrator for information on enabling this feature.

    <b>Feature flag:</b> Enable Notebooks OpenAI Integration

You can now power your code development workflows in DataRobot Notebooks by applying OpenAI large language models for assisting with code generation. With the Azure OpenAI Service integration in DataRobot Notebooks, you can leverage state-of-the-art generative models with Azure's enterprise-grade security and compliance capabilities.

## Use the Code Assistant {: #use-the-code-assistant }

When working in a code cell in a DataRobot Notebook, you can access the Code Assistant by selecting **Assist**.

{% if section(page, "dr-notebooks") %}

![](images/nb-41.png)

{% else %}

![](images/wb-nb-41.png)

{% endif %}

Once selected, complete the prompt by telling the assistant what you want to do. The example below requests Azure OpenAI Service to write code to create a Pandas DataFrame with the Iris dataset and include comments. After completing the prompt, click **Run OpenAI**.

{% if section(page, "dr-notebooks") %}

![](images/nb-42.png)

{% else %}

![](images/wb-nb-42.png)

{% endif %}

Allow some time for the assistant to run&mdash;it then generates the result of the prompt in the cell. The prompt is maintained as the first comment in the cell.

{% if section(page, "dr-notebooks") %}

![](images/nb-43.png)

{% else %}

![](images/wb-nb-43.png)

{% endif %}

As the Code Assistant dynamically materializes the generated code in the cell's code editor, you can choose to speed up the process by clicking **Finish Up!**.

{% if section(page, "dr-notebooks") %}

![](images/nb-44.png)

{% else %}

![](images/wb-nb-44.png)

{% endif %}

You can iterate on the same code cell after generating the initial result. Click **Assist**, provide another prompt in the same code cell, and select **Run OpenAI**. For example, in the cell displayed above, you can prompt the Code Assistant to add comments in Spanish to each line.

{% if section(page, "dr-notebooks") %}

![](images/nb-45.png)

{% else %}

![](images/wb-nb-45.png)

{% endif %}

The resulting cell provides comments for each line of code in Spanish.

{% if section(page, "dr-notebooks") %}

![](images/nb-46.png)

{% else %}

![](images/wb-nb-46.png)

{% endif %}

When generation completes, you can evaluate the helpfulness of the Code Assistant's generation. Select the smiley face to provide an evaluation.

{% if section(page, "dr-notebooks") %}

![](images/nb-47.png)

{% else %}

![](images/wb-nb-47.png)

{% endif %}

From the modal, select whether the generated code was helpful, not helpful, or contains inappropriate or harmful content.

![](images/nb-48.png)
!!! note
    When ingesting data into a Pipeline Workspace via the AI Catalog Import module, if DataRobot cannot intepret the column as a date, time, or timestamp, it is converted to a string column and the data type must be manually updated. Data type detection accepts the same [date and time formats](file-types#date-and-time-formats) as the AI Catalog. However, if **Force Column Types to Strings** on the module's **Details** tab is enabled, date and time values are also converted to string columns.
---
title: Lifecycle management
description: Lifecycle management provides tools and a robust, repeatable process to scale models and manage the lifecycle of models in production environments.

---

# Lifecycle management {: #lifecycle-management }

Machine learning models in production environments have a complex lifecycle, and the use and value of models requires a robust and repeatable process to manage that lifecycle. Without proper management, models that reach production may deliver inaccurate data, poor performance, or unexpected results that can damage your business’s reputation for AI trustworthiness. Lifecycle management is essential for creating a machine learning operations system that allows you to scale many models in production.

The following sections describe how to manage models in production. Be sure to review the [deployment considerations](deployment/index#feature-considerations) before proceeding.

| Topic | Describes... |
|-------|--------------|
| [Deployment inventory (Deployments page)](deploy-inventory) | Coordinate deployments and view deployment inventory. |
| [Manage deployments](actions-menu) | Understand the actions you can take with deployments. |
| [Deployment settings](deploy-settings) | Configure and view deployment settings. |
| [Replace deployed models](deploy-replace) |  Replace the model used for a deployment.  |
| [Set up Automated Retraining policies](set-up-auto-retraining)  | Configure retraining policies to maintain model performance after deploying. |
---
title: Time-series modeling
description: This topic introduces components of time-aware modeling, a recommended practice for data science problems where conditions may change over time.

---

# Time-series modeling {: #time-series-modeling }

!!! info "Availability information"
     Time series modeling is not currently available for DataRobot Self Service users.

Time-series modeling is a recommended practice for data science problems where conditions may change over time. With this method, the validation set is made up of observations from a time window outside of (and more recent than) the time window used for model training. Time-aware modeling can make predictions on a single row, or, with its core time series functionality, can extract patterns from recent history and forecast multiple events into the future.

Topic | Describes...
----- | ------
[What is time-based modeling?](whatis-time) | Learn about the basic modeling process and a recommended reading path.
[Time series workflow overview](ts-flow-overview) | View the workflow for creating a time series project.
[Time series insights](ts-leaderboard) | Explore visualizations available to help interpret your data and models.
[Time series predictions](ts-predictions) | Make predictions with time series models.
[Multiseries modeling](multiseries) | Model with datasets that contain multiple time series.
[Creating clusters](ts-clustering) | Allow DataRobot to identify natural segments (similar series) for further exploring your data.
[Segmented modeling](ts-segmented) | Group series into user-defined segments, creating multiple projects for each segment, and producing a single Combined Model for the data.
[Nowcasting](nowcasting) | Make predictions for the present and very near future (very short-range forecasting).
[Enable external prediction comparison](cyob) | Compare model predictions built outside of DataRobot against DataRobot predictions.
[Advanced time series modeling](ts-adv-modeling/index) | Modify partitions, set advanced options, and understand window settings.
[Time series modeling data](ts-modeling-data/index) | Work with the time series modeling dataset:<br /><ul><li>Creating the modeling dataset</li><li>Using the data prep tool</li><li>Restoring pruned features</li></ul>
[Time series reference](ts-reference/index) | Learn to customize time series projects and view a variety of deep-dive reference material for DataRobot time series modeling, as well as ime series considerations
# Google BigQuery {: #google-bigquery }

## Supported authentication {: #supported-authentication }

- OAuth
- Service account ([public preview](bigq-service-acct))

## Prerequisites {: #prerequisites }

The following is required before connecting to Google BigQuery in DataRobot:

- A Google account [authenticated with OAuth](https://cloud.google.com/bigquery/docs/authorization){ target=_blank }
- A Google BigQuery project

## Set up a connection in DataRobot {: #set-up-a-connection-in-datarobot }

When connecting with OAuth parameters, you must create a new data connection.

To set up a data connection using OAuth:

1. Follow the instructions for [creating a data connection](data-conn#create-a-new-connection)&mdash;making sure the minimum [required parameters](#required-parameters) are filled in&mdash;and [testing the connection](data-conn#test-the-connection). 

2. After clicking **Test Connection**, a window appears. Click **Sign in using Google**.

    ![](images/oauth-google-1.png)

3. Select the account you want to use.

    ![](images/oauth-google-2.png)

4. To provide consent to the database client, click **Allow**.

If the connection is successful, the following message appears in DataRobot:

![](images/snowflake-sso-9.png)

## Required parameters {: #required-parameters }

The table below lists the minimum required fields to establish a connection with Google BigQuery:

Required field | Description |  Documentation
--------------- | ---------- |  -----------
`Projectid`       | A globally unique identifier for your project.  |    [Google Cloud documentation](https://cloud.google.com/resource-manager/docs/creating-managing-projects){ target=_blank }

Learn about additional [configuration options for Google BigQuery](https://cloud.google.com/bigquery/docs/reference/odbc-jdbc-drivers#current_jdbc_driver){ target=_blank } in the installation guide under "Connector Configuration Options".

## Caveats {: #caveats }

- You cannot use multiple Google accounts to authenticate Google BigQuery in DataRobot. Once a Google user is authenticated via OAuth, that Google account is used for all the BigQuery data connections for that DataRobot user.
- If your Google account has a large number of projects, it may take a long time to list schemas, even if the project is filtered with the `projectID` parameter.

{% include 'includes/data-conn-trouble.md' %}
Issue authenticating Google BigQuery<br><br>Need to reset the Google user assigned to authentication      | Locate and remove the `bigquery-oauth` credential.  | <ol><li>In DataRobot, navigate to the **Credentials Management** tab.</li><li>Select the `bigquery-oauth` credential and click **Delete**.</li><li>Select **User Settings > Data Connections**.</li><li>Reauthenticate your Google BigQuery data connection.</li></ol>
Issue authenticating Google BigQuery      | Remove authentication consent in Google Cloud console.  | <ol><li>Navigate to your [Google Account permissions](https://myaccount.google.com/u/1/permissions){ target=_blank }.</li><li>Select **DataRobot** under third-party applications.</li><li>Click **Remove Access**.</li></ol>
---
title: Administrator public preview features
description: Read preliminary documentation for administration features currently in the DataRobot public preview pipeline.
section_name: Administrator
maturity: public-preview
platform: self-managed-only
---

# Administrator public preview features {: #administrator-public-preview-features }

{% include 'includes/pub-preview-notice-include.md' %}

## Available administrator public preview documentation {: #available-administrator-public-preview-documentation }

Public preview for... | Describes...
----- | ------
[Personal data detection](pii-detect) | Run personal data detection on a dataset in the **AI Catalog** and provide a layer of protection against the inadvertent inclusion of this information.
# Version 7.0.2 {: #version-702 }

_April 26, 2021_

The DataRobot v7.0.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.0.0 release notes for:

* [Features introduced in v7.0.0](v7.0.0-aml)

## Updated language localization {: #updated-language-localization }

Localization of the documentation has been updated to include Japanese content for all new 7.0 features.

To change languages, open your [user settings](getting-help) and select a language for your session. The selection remains until you reset it.

![](images/languages-set-pt-jp.png)

## Issues fixed in v7.0.2 {: #issues-fixed-in-v702 }

The following issues have been fixed since Enterprise release v7.0.1:

### Platform {: #platform }

* PLT-3268: Fixes an issue where some permissions were not applied in the UI when switching a user’s role.
* TED-2595: Fixes an issue causing the **Feature Details** chart to not render for **Cross-Class Data Disparity** for protected feature class labels containing underscores (`_`) .

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.

### Feature Discovery {: #feature-discovery }

* SAFER-3632: Fixes an issue with Feature Discovery projects by overriding the Spark configuration from env variable to customize app.

### Time series {: #time-series }

* TIME-7800: Fixes broken model exports for Eureqa GAM models.
## Troubleshooting {: #troubleshooting }

Problem       | Solution  | Instructions
---------- | -----------  | ---------------
When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time.   |  Add all whitelisted IPs for DataRobot. | See [Source IP addresses for whitelisting](data-conn#source-ip-addresses-for-whitelisting). If you've already added the whitelisted IPs, check the existing IPs for completeness.
---
title: Fine-tune models with Eureqa
description: Apply symbolic regression to your dataset in the form of the Eureqa algorithm.

---

# Fine-tune models with Eureqa {: #fine-tune-models-with-eureqa}

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced-experimentation/fine_tuning_with_eureqa/fine_tuning_with_eureqa.ipynb){ .md-button }

DataRobot offers the ability to apply symbolic regression to your dataset in the form of the Eureqa algorithm. Eureqa returns human-readable and interpretable analytic expressions and allows us to incorporate DataRobot's own domain expertise about the problem.

This accelerator shows how the Eureqa algorithm can "discover" the gravitational constant by finding the correct relationship between the variables from a double-pendulum experiment.

This accelerator covers the following activities:

* Apply the Eureqa algorithm to your dataset
* Tune the model's mathematical building blocks to incorporate DataRobot's domain expertise about the problem
* Access the resulting closed-form expression